Multimodal Spatiotemporal Collaborative Positioning Framework for Indoor Environments
| dc.contributor.advisor | El-Sheimy, Naser | |
| dc.contributor.author | Sakr, Mostafa | |
| dc.contributor.committeemember | Gao, Yang | |
| dc.contributor.committeemember | Noureldin, Aboelmagd | |
| dc.contributor.committeemember | O'Keefe, Kyle P. G. | |
| dc.contributor.committeemember | Hassanein, Hossam S. | |
| dc.date | 2019-11 | |
| dc.date.accessioned | 2019-07-12T18:57:15Z | |
| dc.date.available | 2019-07-12T18:57:15Z | |
| dc.date.issued | 2019-07-10 | |
| dc.description.abstract | This thesis proposes and evaluates a unified collaborative and multimodal framework for indoor positioning and mapping using smartphones. The proposed framework aims to harness the potential of collaboration between different nodes for the positioning and mapping tasks, using only smartphones, without assuming the existence of any specific infrastructure. This objective is achieved by first exploring and enhancing the different building blocks of the proposed framework; followed by evaluating the accuracy gains from using a collaborative approach to the positioning problem. The first building block to be studied is the standalone navigation filter. The standard extended Kalman filter, the unscented Kalman filter, and the particle filter were evaluated for node positioning using the pedestrian dead reckoning model as a system model, while the measurement update is achieved using Wi-Fi fingerprinting with a Gaussian process model. The second component of the system is the Wi-Fi radio map. The proposed framework utilizes a new sparse Gaussian process model to represents the Wi-Fi radio map, used for Wi-Fi signal strength-based fingerprinting. The map building algorithm using the proposed model and its performance are presented and discussed. The collaboration between different nodes is examined in detail, and a new family of distributed particle filters for collaborative positioning applications are introduced. The detailed derivation of the filtering equation along with simulation evaluation of the filters are presented. The collaboration model used in the proposed framework is based on the relative range measurements. A ranging device based on ultra-wideband (UWB) technology is designed and implemented to evaluate the framework. The ranging device is based on the DW1000 UWB transceiver from Decawave. The device can reach centimetre-level ranging accuracy and connects to a host microcontroller which controls the flow of ranging messages, computes the range, and communicate with a paired smartphone through Bluetooth Low Energy interface. On the smartphone, a logging application saves the range information from the UWB device along with other sensors data such as accelerometer, gyroscope, magnetometer, pressure, and Wi-Fi signal strength. Along with this software, a simulation environment is developed to model the motion of random nodes inside an indoor environment. This simulator was used in the evaluation of the proposed particle filters family. The thesis concludes by evaluating the proposed framework using multiple test trajectories and different operating scenarios in a challenging indoor environment. | en_US |
| dc.identifier.citation | Sakr, M. (2019). Multimodal Spatiotemporal Collaborative Positioning Framework for Indoor Environments (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/36739 | |
| dc.identifier.uri | http://hdl.handle.net/1880/110624 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | Indoor Positioning | en_US |
| dc.subject | Positioning Framework | en_US |
| dc.subject | Bayesian Estimation | en_US |
| dc.subject | Particle Filtering | en_US |
| dc.subject | Ad-hoc Network | en_US |
| dc.subject | Collaborative Positioning | en_US |
| dc.subject | Ultra-wideband (UWB) Ranging | en_US |
| dc.subject.classification | Engineering | en_US |
| dc.subject.classification | Engineering--Electronics and Electrical | en_US |
| dc.title | Multimodal Spatiotemporal Collaborative Positioning Framework for Indoor Environments | en_US |
| dc.type | doctoral thesis | en_US |
| thesis.degree.discipline | Engineering – Geomatics | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Doctor of Philosophy (PhD) | en_US |
| ucalgary.item.requestcopy | true | en_US |